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Data Engineering
Learning Guide – Information for Students
1. Description
Grade EUROPEAN MASTER ON SOFTWARE ENGINEERING
Module Advanced Software Engineering Aspects
Area
Subject Data engineering
Type Optional
ECTS credits 4
Responsible department DLSIIS
Major/Section/ Unknown
Academic year 2012/2013
Term 1st or 3rd term
Language English
Web site http://pegaso.ls.fi.upm.es/idatos
2. Faculty
NAME and SURNAME OFFICE email
Óscar Marbán Gallego D-4302 [email protected]
Ernestina Menasalvas Ruiz D-4303 [email protected]
3. Prior knowledge required to take the subject
Passed subjects •
Other required learning outcomes
• Design of relational databases
• Implementation of relational databases
• SQL language
4. Learning goals
SUBJECT-SPECIFIC COMPETENCES AND PROFICIENCY LEVEL
Code Competence Level
SC13 To have a vision of the different specific and emergent aspects of the Software Engineering, and to go further in some of them.
S
SC14 To understand what nowadays software engineering procedures can and cannot reach, their limitations and their possible future evolution.
S
Proficiency level: knowledge (K), comprehension (C), application (A), and analysis and synthesis (S)
SUBJECT LEARNING OUTCOMES
Code Learning outcome Related competences
Profi-ciency level
LR1
Faced with a real problem, choose the most appropriate software engineering solution, analyzing the feasibility of this solution, what can and can not get through the current state of development of the chosen solution, and which is expected advances in the near future.
SC13, SC14 3-4
5. Subject assessment system
ACHIEVEMENT INDICATORS
Ref Indicator Related to
LR
I1 Design of a Data Warehouse RA1
I2 Knowledge of the architecture of a Data Warehouse RA1
I3 Implements a Data Warehouse RA1
I4 Obtains implici knowlegde from data RA1
I5 Knowlede of Data Mining techniques RA1
I6 Knowledge of the Data Mining process RA1
I7 Handle Data Mining techniques RA1
I8 Handles Data Mining tools RA1
CONTINUOUS ASSESSMENT
Brief description of assessable activities Time Place
Weight in
grade Project 1 (4 assignements) Weeks 1-8 Home 40%
Project 2 Weeks 8 -
16 Home 40%
Exam Week 16 Class 20%
Total: 100%
GRADING CRITERIA The course is composed by two main parts:
1. Data Warehouse
2. Data Mining
Each part will be evaluated taking into account the evaluation of the final exam and a final Project as follows.
1. Part 1. Project 1 composed by 4 assignments that will have to be presented in the classroom. An exam will also take place and the values of it can be seen in the table.
2. Part 2. Proyect 2 that will have to be presented in the classroom when assigned. There will be also some assignments and a final exam. The values of each part can be seen in the following table.
Projects will be developed by working groups composed by 4 students enrolled in the course.
In order to pass the course the following requirements are needed:
1. to obtain 50/100 points at least
2. It is MANDATORY to do the exam and to complete all the assignments and projects
3. For each Project it is required to obtain at least a qualification of 30% from its final value (see table)
4. For each exam the qualification has to be over the 30% of the maximun value of the exam (see table)
For those who do not pass the course there will be an extra opportunity. To pass this the following requirements are needed:
1. There will be no projects and there will be an exam of each part of the course.
2. The values of each part are as follows:
a. Part 1: 50%
b. Part 2: 50%
GRADING CRITERIA 3. Obtain 50/100 points in the global value
6. Contents and learning activities
SPECIFIC CONTENTS
Unit / Topic / Chapter Section
Related indicators
Chapter 1: INTRODUCTION
1.1 Business Intelligence Introduction I2, I6
Chapter 2: Data warehouse
2.1 Data warehouse architecture I2
2.2 ETL Tools I1, I2
2.3 Multidimensional model I1, I3
2.4 Data warehouse design methodologies I1, I3
2.5 Data warehouse tools I1, I3
2.6 OLAP tools I4
Chapter 3: Data Mining
3.1 KDD process I6
3.2 Data mining techniques I7
3.3 Data mining tools I8
3.4 Data mining methodologies I6
3.5 CRISP-DM I6
7. Brief description of organizational modalities and teaching methods
TEACHING ORGANIZATION
Scenario Organizational Modality Purpose
Theory Classes Talk to students
Seminars/Workshops Construct knowledge through student
interaction and activity
Practical Classes Show students what to do
Placements Round out student training in a professional
setting
Personal Tutoring Give students personalized attention
Group Work Get students to learn from each other
Independent Work Develop self-learning ability
TEACHING METHODS
Method Purpose
Explanation/Lecture Transfer information and activate student cognitive processes
Known as explanation, this teaching method involves the “presentation of a logically structured topic with the aim of providing information organized according to criteria suited for the purpose”. This methodology, also known as lecture, mainly focuses on the verbal exposition by the teacher of contents on the subject under study. The term master class is often used to refer to a special type of lecture taught by a professor on special occasions
Case Studies Learning by analyzing real or simulated case
studies
Intensive and exhaustive analysis of a real fact, problem or event for the purpose of understanding, interpreting or solving the problem, generating hypotheses, comparing data, thinking, learning or diagnosis and, sometimes, training in possible alternative problem-solving procedures.
Exercises and Problem Solving
Exercise, test and practice prior
knowledge
Situations where students are asked to develop the suitable or correct solutions by exercising routines, applying formulae or running algorithms, applying information processing procedures and interpreting the results. It is often used to supplement lectures.
Problem-Based Learning (PBL)
Develop active learning through problem solving
Teaching and learning method whose starting point is a problem, designed by the teacher, that the student has to solve to develop a number of previously defined competences.
Project-Oriented Learning (POL)
Complete a problem-solving project
applying acquired skills and knowledge
Teaching and learning method where have a set time to develop a project to solve a problem or perform a task by planning, designing and completing a series of activities. The whole thing is based on developing and applying what they have learned and making effective use of resources.
Cooperative Learning
Develop active and meaningful learning through cooperation
Interactive approach to the organization of classroom work where students are responsible for their own and their peers’ learning as part of a co-responsibility strategy for achieving group goals and incentives.
This is both one of a number of methods for use and an overall teaching approach, or philosophy.
Learning Contract
Develop independent learning
An agreement between the teacher and student on the achievement of learning outcomes through an independent work proposal, supervised by the teacher, and to be accomplished within a set period. The essential points of a learning contract are that it is a written agreement, stating required work and reward, requiring personal involvement and having a time frame for accomplishment.
11
BRIEF DESCRIPTION OF THE ORGANIZATIONAL MODALITIES AND TEACHING METHODS
THEORY CLASSES Theoretical classes will proceed participatively, with certain ones requiring a previous study by the student of some bibliography
PROBLEM-SOLVING CLASSES
Some classes will be dedicated to problem solving and modelling in the classroom to have a discussion on the issues modeled at the end of the classroom.
PRACTICAL WORK
INDIVIDUAL WORK Individual works will be announced in classroom for the course.
GROUP WORK The main learning activity in the course will consist on a team project in teams of 4 students, applying practically the concepts discussed in the classroom to the project.
PERSONAL TUTORING
The classes dedicated to follow-up of team projects will serve a group tutoring, while other assignments will be tutored at the student request.
12
8. Teaching resources
TEACHING RESOURCES
RECOMMENDED READING
Building the data warehouse. W. H. immnon. 1996. Willey
Managing the Data Warehouse. W. H. Immon. 1997. Willey
Building the operational Data Store. W. H. Immon. 1999. Willey
Exploration Datawarehouse. W. Immon. 2000. Willey
Improving Data Warehouse and Business Information Quality. Methods for reducing cost and increasing profits. L. English. 1999 Willey
Data Mining Techniques for Marketing, Sales, and Customer support. Michael J. A. Berry and Gordon Linoff. 1997. Willey
Data Mining Solutions: Methods for solving Real-World Problems. C. Westphal, T. Blaxton. 1998. Willey
Mastering Data mining . The art and science of Customer relationship management. M. Berry, G. Linoff. 2000. Willey
The data warehouse lifecycle toolkit. R. Kimball. 2000. Willey
WEB RESOURCES Subject web site (http://pegaso.ls.fi.upm.es)
EQUIPMENT Room
Group work room
13
9. Subject schedule Week Classroom activities Lab
activities Individual work Group work Assessment
activities Others
Week 1 (6 hours)
• 1.1 Business Intelligence introduction (2 hours)
• • Independent work (2 hours) • Group work (2 hours)
• •
Week 2 (7 hours)
• 2.1 Data warehouse architecture (2 hours)
• • Independent work (2 hours) • Group work (3 hours)
• •
Week 3 (7 hours)
• 2.2 ETL Tools (1 hours) • • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hours) •
Week 4 (7 hours)
• 2.3 Multidimensional model (2 hours)
• • Independent work (2 hours) • Group work (3 hours)
• •
Week 4 (7 hours)
• 2.3 Multidimensional model (2 hours)
• • Independent work (2 hours) • Group work (3 hours)
• •
Week 6 (7 hours)
• 2.5 Data warehouse tools (1 hours)
• • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hora) •
Week 7 (7 hours)
• 2.6 OLAP Tools (1 hours) • • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hora) •
Week 8 (7 hours)
• 2.4 Data warehouse design methodologies (1 hours)
• • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hora) •
14
Week 9 (6 hours)
• 3.1 KDD process (2 hours) • • Independent work (2 hours) • Group work (2 hours)
• •
Week 10 (7 hours)
• 3.1 KDD process (2 hours) • • Independent work (2 hours) • Group work (3 hours)
• •
Week 11 (7 hours)
• 3.2 Data mining techniques (2 hours)
• • Independent work (2 hours) • Group work (3 hours)
• •
Week 12 (7 hours)
• 3.2 Data mining techniques (1 hours)
• • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hora) •
Week 13 (7 hours)
• 3.3 Data mining tools (1 hours)
• • Independent work (2 hours) • Group work (2 hours)
• Presentation (1 hora) •
Week 14 (7 hours)
• 3.4 Data mining methodologies (2 hours)
• • Independent work (2 hours) • Group work (3 hours)
• •
Week 15 (6 hours)
• 3.5 CRISP-DM (2 hours) • • Independent work (2 hours) • Group work (2 hours)
• •
Week 16 (7 hours)
• 3.5 CRISP-DM (1 hours) • • Independent work (2 hours) • Group work (3 hours)
• Presentation (1 hora) •
Week 17 (2 hours)
• • • • • Final exam (2 hours) •
• • • • • •
Note: Student workload specified for each activity in hours